Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT

Junguo Bian, Jeff Siewerdsen, Xiao Han, Emil Y. Sidky, Jerry Ladd Prince, Charles A. Pelizzari, Xiaochuan Pan

Research output: Contribution to journalArticle

Abstract

Flat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of thework is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments using a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles and conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.

Original languageEnglish (US)
Pages (from-to)6575-6599
Number of pages25
JournalPhysics in Medicine and Biology
Volume55
Issue number22
DOIs
StatePublished - Nov 21 2010

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ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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